Image interpolation for medical imaging

ABSTRACT

Presented are systems and methods that allow for interpolation of a 3-D volume from arbitrarily oriented 2-D medical images. The interpolation of 3-D volume from arbitrarily oriented 2-D images reduces or eliminates most constraints on image acquisition thereby allowing for, inter alia, freehand manipulation of an image acquisition device (e.g. an ultrasound transducer). Related utilities involve the use of prior information about a specific object of interest to interpolate a surface (e.g., 3-D surface) of the object from limited information obtained from very few 2-D images.

FIELD

The present disclosure pertains to the field of medical imaging, andmore particular to the registration of arbitrarily aligned 2-D images toallow for the generation/reconstruction of a 3-D image/volume.

BACKGROUND

Medical imaging, including X-ray, magnetic resonance (MR), computedtomography (CT), ultrasound, and various combinations of these and otherimage acquisition modalities are utilized to provide images of internalpatient structure for diagnostic purposes as well as for interventionalprocedures. Often, it is desirable to utilize multiple two-dimensional(i.e. 2-D) images to generate (e.g., reconstruct) a three-dimensional(i.e., 3-D) image of an internal structure of interest.

2-D image to 3-D image reconstruction has been used for a number ofimage acquisition modalities (such as MRI, CT, Ultrasound) and imagebased/guided procedures. These images may be acquired as a number ofparallel 2-D image slices/planes or rotational slices/planes, which arethen combined together to reconstruct a 3-D image volume. Generally, themovement of the imaging device has to be constrained such that only asingle degree of freedom is allowed (e.g., rotation). This single degreeof freedom may be rotation of the equipment or a linear motion. Duringsuch a procedure, the presence any other type of movement will typicallycause the registration of 2-D images in 3-D space to be inaccurate. Thispresents some difficulties in handheld image acquisition where rigidlyconstraining movement of an imaging device to a single degree of freedomis difficult if not impossible. Further constraining an imaging deviceto a single degree of freedom may also limit the image information thatmay be acquired. This is true for handheld, automated and semi-automatedimage acquisition. Depending upon the constraints of the imageacquisition methods, this may limit use or functionality the acquisitionsystem for 3-D image generation.

Many 3-D reconstruction techniques currently require a significantnumber of 2-D images in order to achieve a reasonably good resolution.This typically results in a slow scan process and/or slow 3-D imagereconstruction. The requirement of a large number of 2-D images may alsolead to unnecessary workflow issues, causing hindrance to workflowand/or patient discomfort. Further, in many imaging situations, theactual region of interest is generally much smaller than the actualimage acquired, resulting in unnecessary computational overheads forinterpolation at regions outside the object of interest. During amedical procedure such as image based biopsy or therapy, a user isgenerally interested in only one organ and not in the backgroundinformation. Further, in operating room environments time allocated forimaging procedures and/or image guide procedures is minimal due to timepressures on surgeons to undergo more procedures in allocated time.Accordingly, it is desirable to perform 3-D image generation in a mannerthat reduces the constraints on image acquisition and allows for quicklygenerating a 3-D image, while also providing sufficient resolution toperform a desired procedure.

SUMMARY

The invention presented herein solves a number of problems using novelsystems and methods (i.e., utilities). These utilities allow forinterpolation of a 3-D volume from arbitrarily oriented 2-D images. Theinterpolation of 3-D volume from arbitrarily oriented 2-D images reducesor eliminates most constraints on image acquisition thereby allowingfor, inter alia, freehand manipulation of an image acquisition device(e.g. an ultrasound transducer). In one arrangement, the utilitiesmaintain the relationship between acquired 2-D images and a prospective3-D image volume via a tracker that tracks the coordinates andorientation of the imaging plane of each 2-D image. This can be doneusing any type of tracker including but not limited to optical, magneticand/or mechanical trackers. It will be appreciated the interpolationmethods are not limited by the method of tracking, imaging modality ortype of procedure. The interpolation method is not limited to ultrasoundbut applicable to other modalities such as MRI, CT, PET, SPECT and itsfusion.

A related utility involves the use of prior information about a specificobject of interest to interpolate a surface (e.g., 3-D surface) of theobject from limited information obtained from very few 2-D images. Inthis utility, shape statistics of a structure/object of interest arecomputed beforehand and stored in a computer. The prior shape statisticsmay include a mean shape of the object and/or the statistics over alarge number of samples of the object of interest. This allows forgeneration of a shape based deformation model. Deformation of the shapebased model may be guided (in real time) and constrained by the actualdeformation statistics from the shape priors. The shape model is used todeform the mean shape of the object to the available 2-D images/planesthrough, for example, hierarchical optimization over the modes ofvariation of the object. The deformation may be guided by intensitygradients over the available 2-D images of arbitrary orientation. Theinventive aspects may be implemented in processing systems that areintegrated into medical imaging devices/systems and/or be implementedinto stand-alone processing systems that interface with medical imagingdevices/systems.

In one aspect, the utility is provided for allowing interpolation and/orreconstruction of a 3-D image based on arbitrarily oriented 2-D imageplanes obtained from a 2-D imaging device. The method includes obtainingat least first and second 2-D images of an internal object of interest.Each 2-D image includes an image plane and a first 3-D point ofreference. Typically, the image planes and 3-D points of reference aredifferent for each image. Pixel information (e.g., intensityinformation) for each of the 2-D images is translated into a common 3-Dvolume. Based on the pixel information disposed within a common 3-Dvolume, a 3-D image of the object of interest is generated.

Translating the pixel information of the 2-D images into a common 3-Dvolume may include applying a coordinate transform to the 2-D images. Asmay be appreciated, this may require obtaining or otherwise determiningvector information for use with the images. Typically, the 3-D point ofreference may be provided by a tracker assembly that provides locationinformation for a medical imaging device that generates the 2-D image.Furthermore, information regarding the depth and orientation of theimage in relation to the reference point may also be obtained.Accordingly, a normal may be determined for the plane of the 2-D image.Utilizing this information, a transformation matrix may be applied tothe vector information associated with the 2-D images. Accordingly, suchtransformation may allow for translating pixels within the 2-D imagesinto the common 3-D volume. The method may further include interpolatingpixel intensities from the 2-D images to discrete locations within the3-D volume.

It will be appreciated that the ability to translate arbitrary 2-Dimages into a common 3-D volume avoids the need for a constrained imageacquisition procedure. That is, no mechanical assembly is required forobtaining 2-D images. As a result, an imaging device may be freelymaneuvered (e.g., freehand), which may result in better workflow.

Once a 3-D image is generated from the pixel information of the 3-Dvolume, the user may selectively obtain additional 2-D images. Thisinformation may be incorporated into the 3-D image. This may allow auser to focus on specific parts of anatomy by acquiring samplesnon-uniformly. This provides the user with flexibility to acquire imagesin better resolutions at some regions while acquiring imaging at lowerresolution at others. This not only enhances the resolution at desiredlocations but also reduces the unnecessary computational overhead ofacquiring samples from locations where lower resolution may be adequate.Likewise, this may reduce the time required to adequately scan an objectof interest.

In a further aspect, a shape model may be fit to pixel information in acommon 3-D volume in order to define a 3-D surface of an internal objectof interest. Accordingly, a plurality of arbitrarily aligned 2-D medicalimages may be obtained for an internal object of interest. Pixelintensity information from each of the 2-D images may be translated intoa 3-D volume. Accordingly, a predefined shape model may be fit to thepixel intensity information such that the shape model defines a 3-Dsurface for the object of interest.

In one arrangement, the predefined shape model may be generated based onan average of a population of a corresponding object. For instance, forprostate imaging, such a shape model may be generated from a trainingset of prostate images. Likewise, shape statistics for the training setpopulation may be utilized in order to fit the predefined shape model tothe pixel intensity information. In a further arrangement, the shapemodel may be constrained by boundaries identified within the 2-D images.In this regard, segmentation may be performed on the 2-D images in orderto identify one or more boundaries therein. These boundaries may then beutilized as constraints for the shape model.

In one arrangement, a principle component analysis is utilized toidentify the largest modes of variation for the shape model.Accordingly, by identifying the largest modes of variation the shapemodel may be fit to the pixel intensities utilizing fewer variables.

In another aspect, the utility is provided for registering currenttwo-dimensional images with previously stored three-dimensional images.In this regard, during an imaging procedure as a user manipulates animaging instrument (e.g., freehand) previous few image frames may bekept in memory. These frames may then be used for motion correction ofthe current imaging device location relative to a previouslyacquired/stored three-dimensional image. Such motion correction may benecessitated due to patient movement, motion of anatomical structures tothe procedure and/or to device movement or miscalibration. Initially, aseries of two-dimensional images are obtained. The series oftwo-dimensional images may be obtained in real-time such that the lastimage is the most current image. Pixel information from each of thetwo-dimensional images may be translated into common three-dimensionalvolume in order to generate a current three-dimensional image using theseries of two-dimensional images. This current three-dimensional imageof the object of interest may be utilized to align the most currenttwo-dimensional with the previously stored three-dimensional image.

In one arrangement, use of the current three-dimensional image mayinclude registering the current three-dimensional image with theprevious three-dimensional image in order to identify orientationcorrespondence therebetween. Accordingly, the most currenttwo-dimensional image may be aligned based on this information.

In one arrangement, the two-dimensional images are maintained and bufferin the computerized imaging device. Such images may be maintained on afirst and first-out basis. For instance, the buffer may maintain theprevious five to 10 images wherein the oldest image is continuouslyreplaced by the most current image.

Generally, all the utilities also allow for using more informationduring navigation. That is, the presented utilities use more informationthan most conventional 2-D ultrasound based navigation systems. This isdone by keeping some of the previous image frames in a buffer. Theprevious frames together with the current frame provide at leastpartially 3-D information and thus provide a better and more robustsolution when correlating the current 2-D image with an earlier acquired3-D.

The utilities also allow for non-rigid motion correction. That is, inaddition to correlating a current 2-D image with 3-D scan, theadditional information can also be used to perform non-rigidregistration Since more frames are used, more deformation can becaptured by the deformation model.

The utilities also permit shape based boundary interpolation. In thisregard a mean shape model may be fit to the boundaries of an objectbased on much less information compared to an explicit segmentationmethod. The mean shape is used as the template for fitting onto thesurface. By definition, mean shape is more similar to the population andthus, is ideal as the deformation template. Further usage of actualdeformation statistics from the training samples (i.e., used to form theshape model) corresponding to the anatomy of the object in considerationrepresent actual deformation modes of the objects in the population andthus, the results are representative of the population.

Further, the use of reduced dimensionality of the shape model may allowfor faster image generation speeds. In one arrangement, the presentedutilities use a principal component analysis (PCA), which identifies andoptimizes over a smaller number of parameters while capturing populationstatistics well. Further hierarchical optimization over modes ofvariations ensures that coefficients for larger modes get optimizedfirst and then smaller modes of variations follow. This adds torobustness and stability of utilities and also avoids small localminima.

The utilities are also adaptable to image fusion with other imagingmodalities. That is, the utilities are easily extensible to includeimage fusion (e.g., ultrasound images) with other modalities such asElastography, MR spectroscopy, MRI, etc. The interpolated frames in the3-D space can be correlated with the images from other modality and thecorrespondence computed between them.

The utilities also provide more information for tracking in 3-D. Due todynamic nature of the placement of 2-D data in 3-D and arbitrariness ofthe orientation, the 2-D frames can be kept in memory buffer of thecomputer and called upon to correct for motion compensation in 3-D. Mostcurrent techniques only use the live 2-D image to correlate the currentfield of view, which is an ill-conditioned problem. Extra informationpresent in form of previous frames makes it better conditioned.

The utilities can be used to compute local deformation at every placeinside the object of interest, which can be used for clinicalinterpretation about the nature and progression of a disease. Manydiseases including cancer manifest themselves into change in tissuedeformation characteristics or change in tissue volume over time, whichcan be easily captured through computation of local deformation.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a medical imaging system utilized for ultrasoundimaging.

FIGS. 2A and 2B illustrate acquisition of medical images having a singledegree of freedom.

FIGS. 3A-3D illustrate arbitrarily aligned 2-D images and the use ofthose images to identify the boundaries of an internal object ofinterest.

FIGS. 4A and 4B illustrate a coordinate system for a 2-D imaging plane.

FIG. 5 illustrates a process for reconstructing a 3-I) image fromarbitrarily oriented 2-D images.

FIG. 6 illustrates a process for use of arbitrarily orienting 2-D imagesin conjunction with a shape model.

FIG. 7 illustrates generation of a shape model for identifyingboundaries of a 3-D object.

DETAILED DESCRIPTION

Reference will now be made to the accompanying drawings, which assist inillustrating the various pertinent features of the present disclosure.Although the present disclosure is described primarily in conjunctionwith transrectal ultrasound imaging for prostate imaging, it should beexpressly understood that aspects of the present invention may beapplicable to other medical imaging applications. In this regard, thefollowing description is presented for purposes of illustration anddescription.

As presented, the invention is directed towards systems and methods forinterpolation and reconstruction of a 3-D image from 2-D imageplanes/frames/slices obtained in arbitrary orientation during, forexample, an unconstrained scan procedure. Also included is a method foradaptively improving interpolation based on dynamic addition of more 2-Dframes to an imaging buffer. Systems and methods are also provided forusing shape priors to interpolate the surface of an internal object ofinterest using intensity information of a limited number of 2-D imageplanes that have the object of interest in their field of view. Further,a combination of the above systems and methods allow arbitrary (e.g.,freehand) scanning of a few 2-D images and then fitting a surface forthe object of interest using intensity information from the scannedimages.

The reconstruction method pertains to all types of 2-D image acquisitionmethods under various modalities and specifically for 2-D imageacquisition methods used while performing an image-guided diagnostic orsurgical procedure. It will be appreciated that such procedures include,but are not limited to ultrasound guided biopsy of various organs, suchas prostate (trans-rectal and trans-perineal), liver, kidney, breast,etc., brachytherapy, ultrasound guided laparoscopy, ultrasound guidedsurgery or an image-guided drug delivery procedures.

As noted above, most current methods for reconstructing a 3-D image from2-D image planes assume some type of uniformity (e.g., constraint) inimage acquisition. For example, most previous methods assume (orrequire) that the 2-D images be obtained as parallel slices or aredisplaced from each other through an angle while meeting at one fixedaxis.

FIG. 1 illustrates a transrectal ultrasound probe 10 that may beutilized to obtain a plurality of two-dimensional ultrasound images ofthe prostate 12. As shown, the probe 10 may be operative to scan an areaof interest. The probe 10 may also include a biopsy gun that may beattached to the probe. Such a biopsy gun may include a spring drivenneedle that is operative to obtain a core from desired area within theprostate and/or deliver medicine (e.g., a brachytherapy seed) to alocation within the prostate.

In an automated arrangement, the probe may be affixed to a positioningdevice (not shown) and a motor may sweep the transducer of theultrasound probe 10 over a radial area of interest (e.g., around a fixedaxis 70 of FIG. 2A). Accordingly, the probe 10 may acquire plurality ofindividual images while being rotated through the area of interest. Eachof these individual image slices may be represented as a two-dimensionalimage. Alternately, the probe 10 may be linearly advanced to obtain aplurality of uniformly spaced images as is illustrated in FIG. 2B. Inboth instances, the resulting 2-D image sets may be registered togenerate a three-dimensional image. In order to generate a highlyaccurate 3-D reconstruction, previous interpolation techniques havetypically depended heavily on the tolerances on deviation from theassumptions (i.e., that all images are fixed except for a single degreeof freedom). However, it is often desirable to utilize a handheld probeto acquire images, for example, just prior to performing a procedure.

Such handheld acquisition, however, often introduces multiple degrees offreedom into the acquired 2-D images. For example, FIG. 3A illustrates aplurality of 2-D images 80 a-n acquired for an object of interest (e.g.,prostate) where the images are not aligned to at least one axis.Further, while performing a procedure or delivering drug to specificpart of an organ (or focused surgery such as focusing in narrow regionssuch as in high frequency ultrasound methods), a user may not need 3-Dinformation in same resolution inside and outside the object.Specifically, the procedure requirements may not need the user to gothrough a tedious and constrained acquisition of 2-D images, which maytake longer for unnecessarily high resolution everywhere in areconstructed 3-D image, or yield a uniformly low-resolution image.Instead, higher resolution at the region of interest and lowerresolution at other places may in some instances be sufficient.

Aspects of the presented systems and methods provide such an optionwhile also allowing for conventional and constrained interpolation andreconstruction strategies. In this method, 2-D images are obtained in anunconstrained fashion (e.g., using handheld imaging devices) while theimaging device is manipulated to scan the object. The user may scan theobject in a freehand fashion in various different orientations. See,e.g., FIG. 3A. The orientation and location of the imaging planes aremeasured using a mechanical or magnetic tracker 14, which may beoff-the-shelf or designed specifically for that application. See FIG. 1.The position of the tracker 14 is recorded in relation to a knownreference by a reading device 16, which outputs the identified locationof the tracker 14 to an imaging system 30 that also receives images fromthe imaging device 10. The configuration of the reading device maydepend upon the type of tracker 14 utilized. For instance, whenutilizing a magnetic tracker, the reader may be a magnetic field sensor,which is interfaced to a computer or recording device via an interfacebox. Mechanical trackers may have a combination of rotary and linearencoders to track the transducer attached to the tracker. Opticaltrackers have attachment containing LEDs and their position is space iscomputed using images captured from two cameras at different locations.

The imaging system 30 is operative to correlate the recorded position ofthe tracker and a corresponding acquired image. As will be discussedherein, this allows for utilizing non-aligned/arbitrary images for 3-Dimage reconstruction. That is, the imaging system 30 utilizes theacquired images to populate the 3-D image volume as per their measuredlocations. In addition to reconstructing the 3-D volume after images areacquired, the method also allows for dynamic refinement at desiredregions. That is, a user may acquire additional images at desiredlocations and the reconstruction method interpolates the additionalimages into the 3-D volume.

While a significant number of 2-D images typically need to be acquiredto generate a 3-D image of reasonably good resolution for an object ofinterest, it can be time consuming to obtain enough 2-D images to getsuch resolution. Moreover, it remains a challenge to extract surfacefrom the images in a short time so as to be usable. Without extractionof surface of the object, some of key advantages of 3-D images are lostsince the user can no longer visualize the anatomy in a 3-D spacewithout the background being suppressed. As a result, the correspondenceof a 2-D live image during the procedure with the previously acquired3-D image is rendered useless. In order to take full advantage of 1)live image corresponding to a 3-D anatomical object imaged previously,the operator has to 1) acquire a series of 2-D images at a reasonablygood resolution and 2) after reconstruction, extract the surface ofanatomical object from the 3-D image. Both processes are time-consumingif meaningful results are to be obtained.

A second aspect of the present invention addresses this issue byallowing the user to collect images in a simplified manner (e.g.,freehand, just before a procedure). In this aspect, instead ofinterpolating the pixel intensity values in 3-D image volume from thescanned 2-D image planes, the object itself is interpolated using shapepriors. Shape priors refer to a set of information 60 collectedpreviously for a particular anatomical object and includes the meanshape of the object along with statistical information. The statisticalinformation provides the modes of variations in the shape of the objectand represents anatomically meaningful interpretations of the shapevariability within the population.

Interpolation of 2-D Images in Arbitrary Orientation.

While imaging in freehand or using any constrained or unconstrainedmethod, the tracker provides full information about the 2-D plane beingimaged. A normal to the imaging plane and a reference line passingthrough a known point in the imaging plane are sufficient to describethe imaged plane fully based on the geometry of the imaging device. Forexample, as illustrated in FIGS. 4A and 4B, which illustrate an end-fireTRUS ultrasound transducer 10, the position of the center of thetransducer tip (i.e., provided by a tracker) along with the normal tothe image plane 22 and the central axis 24 of the transducer 20 issufficient to place the 2-D image in a 3-D volume, assuming that theother characteristics such as the depth setting and geometry(rectangular, fan, etc) of 2-D imaging plane are known. In such a case,the tracker output can be measured to provide the location andorientation of the imaging equipment, which in turn provides the normalto imaging plane, central axis 24 of the transducer 20 and the tip ofthe transducer 20. Based on geometry of the 2-D image observed, itscorresponding location in 3-D can be determined using the methodpresented.

As illustrated, FIG. 4B shows a 2-D imaging slice 22 and its associatedco-ordinate system. Letting X_(i)′=[x_(1i)′ x_(2i)′ x_(3i)′] representthe co-ordinate system of i-th 2-D image acquired as shown in thefigure, where x₃′ faces out of plane of the 2-D image slice 22. Then, inframe of reference of the coordinates of image, x_(3i)′ is always zeroand the center of transducer tip is at the origin.

FIG. 4A illustrates the co-ordinate system for the 3-D image, whereorigin is selected by the user at the time of initialization of animaging scan by pointing roughly to the center of the object 28 to beimaged. As shown in the figure, let X=[x₁ x₂ x₃]^(t) represent thecoordinates in frame of reference a resulting 3-D image.

In order to interpolate the images to fill a 3-D space and therebyconstruct a 3-D image, the 2-D image coordinate system has to be placedin a 3-D image coordinate system. Such a process 500 is illustrated inFIG. 5. Initially, 2-D images, which may have arbitrary orientations,are obtained (502). Tracking information for each image is likewiseobtained (504). Then, each pixel in each 2-D image is placed (506) in acorresponding location in a 3-D volume. This is done first by placingthe origin of each 2-D image in a frame of reference of a common 3-Dvolume and then applying the coordinate transformation. The coordinationinformation is obtained by tracking the location and orientation of theimaging transducer using an unconstrained tracker such as a magnetictracker, freehand mechanical tracker, optical tracker, etc. Theco-ordinate transformation simply involves a rotation matrix obtained bysolving the following set of linear equations:

Rn_(i)′=n,

Rt_(i)′=t,   Eq. 1

Rb_(i)′=b.

where, n_(i)′=[0 0 1]^(t) is the unit vector in direction of x_(3i)′,t_(i)′=[0 1 0]^(t) represents the unit vector in the direction of x_(2i)and b_(i)′=[1 0 0] is the unit vector in direction of x_(1i)′ in frameof reference of the 2-D image. n and t represent the measured normal andtangent unit vectors for the i-th slice and b represents the unitbi-normal representing the cross-product n×t. This information isprovided by the tracker.

The rotation matrix R computed above is used to compute thetransformation of the coordinate system from the coordinates of the 2-Dimage into coordinates of the 3-D image. Letting O be the origin of theframe of reference of the 3-D image and O′ be the origin of frame ofreference of the 2-D image. Then, the overall transformation ofcoordinate point (x_(i)′, y_(i)′, z_(i)′) from the coordinate system ofthe 2-D image into the frame of reference of the 3-D image is given by:

$\begin{matrix}{{\begin{pmatrix}x_{i} \\y_{i} \\z_{i} \\1\end{pmatrix} = {\begin{bmatrix}\; & \; & \; & {\Delta \; x_{i}} \\\; & R & \; & {\Delta \; y_{i}} \\\; & \; & \; & {\Delta \; z_{i}} \\0 & 0 & 0 & 1\end{bmatrix}\begin{pmatrix}x_{i}^{\prime} \\y_{i}^{\prime} \\z_{i}^{\prime} \\1\end{pmatrix}}}{\text{where},{\begin{pmatrix}{\Delta \; x_{i}} & {\Delta \; y_{i}} & {\Delta \; z_{i}}\end{pmatrix} = {O - {O^{\prime}.}}}}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

At this time, the real location of the 2-D pixels is located (508) in acommon 3-D volume. The transformed coordinates do not, in general, lieon a discrete lattice and the intensities at the transformed “real”coordinate locations need to be interpolated (510) onto the neighboringdiscrete locations. First, the neighboring discrete locations arecomputed and variables defined as follows:

$\begin{matrix}{x_{li}^{d} = {{{mod}\mspace{11mu} \left( x_{i} \right)} = {{largest}\mspace{14mu} {integer}\mspace{14mu} {smaller}\mspace{14mu} {than}\mspace{14mu} {x_{i}.}}}} \\{{x_{hi}^{d} = {{x_{li}^{d} + 1} = {{smallest}\mspace{14mu} {integer}\mspace{14mu} {greater}\mspace{14mu} {than}\mspace{14mu} x_{i}}}},{{{{if}\mspace{14mu} x_{li}^{d}} \neq x_{i}};}} \\{{= x_{li}^{d}},{{{if}\mspace{14mu} x_{li}^{d}} = x_{i}},{and}} \\{{{dx}_{li} = {x_{i} - x_{li}^{d}}},} \\{{dx}_{hi} = {x_{hi}^{d} - {x_{i}.}}}\end{matrix}$

Letting y_(li) ^(d), y_(hi) ^(d), dy_(li), dy_(hi), z_(li) ^(d), z_(hi)^(d), dz_(li) and dz_(hi) be defined similarly for x_(i), y_(i) andz_(i), respectively. Then, initializing all the intensities in thereconstructed image to be equal to zero and define the following weightsto intensities of pixels for 2-D frame i as shown below:

w _(i)(x _(li) , y _(li) , z _(li))=(1−dx _(li))(1−dy _(li))(1−dz _(li))

w _(i)(x _(li) , y _(li) , z _(hi))=(1−dx _(li))(1−dy _(li))(1−dz _(hi))

w _(i)(x _(li) , y _(hi) , z _(li))=(1−dx _(li))(1−dy _(hi))(1−dz _(li))

w _(i)(x _(li) , y _(hi) , z _(hi))=(1−dx _(li))(1−dy _(hi))(1−dz _(hi))

w _(i)(x _(hi) , y _(li) , z _(li))=(1−dx _(hi))(1−dy _(li))(1−dz _(li))

w _(i)(x _(hi) , y _(li) , z _(hi))=(1−dx _(hi))(1−dy _(li))(1−dz _(hi))

w _(i)(x _(hi) , y _(hi) , z _(li))=(1−dx _(hi))(1−dy _(hi))(1−dz _(li))

w _(i)(x _(hi) , y _(hi) , z _(hi))=(1−dx _(hi))(1−dy _(hi))(1−dz _(hi))

Letting I_(i)(x, y, z) be the intensity of the transformed image atcoordinate (x, y, z) based on i-th 2-D image frame. Then, the intensityat a pixel (voxel) location (x,y,z) can be dynamically computed as:

$\begin{matrix}{{I\left( {x,y,z} \right)} = \frac{\sum\limits_{i}{w_{i}{I_{i}\left( {x,y,z} \right)}}}{\sum\limits_{i}w_{i}}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

where, the summation is over all the frames (i's in above equation) thatcontribute to the pixel (x,y,z). This results in a space interpolation(512) of 3-D image voxels. As more and more 2-D frames contribute to thesame pixel location in discrete lattice of the 3-D image volume, theintensity gets refined to produce better results.

Once the images are acquired, a Gaussian interpolation filter or otherappropriate filter (514) with appropriate window size (3³ to 7³, basedon image resolution) can be used to interpolate (516) the results topixels that have not been initialized using Eq. 1 above. The result isthat a 3_D image volume may be constructed (518) from the acquiredimages. If the user wishes to scan portions of image in betterresolution, they may acquire more images from the area of interest andthe method will dynamically update the intensity values in that locationas per Eq. (3).

The method thus, interpolates a 3-D image from 2-D images that areacquired in arbitrary orientations and thereby permits freehandscanning. This may improve the workflow, as it removes need for anylocking or constrained tracker. In addition, the dynamic interpolationallows the user to dynamically improve the resolution by acquiring moreimages from a particular are of interest. The flexibility in choosingdifferent resolution for different regions is another key advantage ofthe method.

Using Shape Information to Interpolate the Object Surface.

Shape priors are utilized to perform interpolation of a surface for the3-D image volume. In this method, first, shape statistics are generatedand analyzed from a number of actual data sets from the anatomicalobject in question (e.g., prostate). To do this, a number of images ofthe anatomical object are collected. The boundaries of the object ofinterest are then extracted either manually by expert segmentation, orusing a semi-automatic or automatic method. The surfaces are thennormalized so as to remove the translation, rotation and scalingartifacts. The normalized images are then averaged together by computingmean position of each vertex in the template chosen for computing themean shape. The shape obtained is run through the same process untilconvergence. This provides the mean shape of the object and this shapeis then registered with all the other shapes in the sample dataset. Theregistration provides deformation details at each vertex of the imageand the statistics of the same are then used to drive the fitting of themean shape into the shape of the subject.

Previously computed statistics may be used to deform the mean shape tofit the 3-D image volume, which may be sparse due to being generatedusing very few 2-D image planes and/or due to the 2-D images beingobtained in different orientations. Conventional segmentation techniquesfor extraction of surface rely heavily on the resolution of images.Although 2-D segmentation may be possible on individual frames, thecombination of these frames together to generate a surface in 3-D usinga heuristic approach typically produces artifacts in surfaceinterpolation. Further, due to the limited number of 2-D imagesboundaries of the 3-D volume based solely on the 2-D images may notprovide a useful estimation of the actual boundary of the object. SeeFIG. 3B. With the presented method however, limited information issupported by the prior knowledge about the shape and the shape priorinformation is used to fit an entire surface in 3-D such that it stillrepresents the boundaries in the sampled 2-D image but is definedentirely. FIG. 6 shows the overall scheme for combination of the twomethods. The images are acquired.

As shown, the process (600) includes acquiring 2-D images (602) andtracking information (604). These elements are utilized tointerpolate/reconstruct (606) a 3-D image volume (608), as set forthabove. Object shape statistics (610) associated with the object of the3-D image volume (608) are utilized to fit (612) the mean shape to the3-D image volume. This generates an object surface (614) that may beoutput to, for example, a monitor 40 (See FIG. 1) and/or utilized for animage guided procedure or therapy.

The first step is to generate population statistics including a meanshape from a collection of samples from population of the object ofinterest. This may be done once and may be performed off-line (i.e., ata time before an imaging procedure). The population may correspond to aspecific organ, for example: prostate, in which case, a number of imagesacquired from prostates of different individuals may be used as samples.Larger sample size can capture more variability in the population andhence, a reasonably large sample size should be used to capture theshape statistics. The population may consist of images from differentmodality.

FIG. 7 shows the computation of mean shape and capturing the shapestatistics from the training dataset (702). First, the surface of theobject is extracted/segmented (704) for all the images in the sampledataset. This may be done manually, semi-automatically or automatically.After extraction of surface (706) from all the images throughsegmentation, one image that best represents the population is chosen(708) as template image (710). The surface of the template isaligned/registered 714 to the current target image 712 using rotation,translation and anisotropic scaling and a mean surface is computed 716.The mean surface is then deformed into every other image in the trainingset and the mean surface is updated by computing an average again 720 toseparate an updated mean shape 722. This process is repeated untilconvergence to generate a final mean shape 730. A more completedescription of computing a mean shape is set forth in co-pending U.S.patent application Ser. No. 11/740,807, entitled, “Improved System andMethod for 3-D Biopsy,” the contents of which are incorporated herein byreference.

To generate population statistics, the mean surface is warped into allthe surfaces in the training set such that the vertex locations ofundeformed mean shape and deformed mean surface are known. Letting V_(i)represent the set of vertices for image i, and V_(μ) represent the setof vertices for the mean shape, then the population co-variance matrixis computed as S=(V_(i)−V_(μ))^(t)(V_(i)−V_(μ)). The eigen-vectors ofthe covariance matrix then represent the modes of variations of theshape within the training data. The eigenvalues can then be arranged indescending order such that the the eigen vectors representing first fewmodes of variations represent most of the variability in the population.Specifically, let Sλ=pλ, where e represents the vector of all the eigenvalues and p represents the corresponding eigenvectors. Then, first feweigenvectors be chosen to represent most of variability in thepopulation. In addition, this knowledge can be used to generate moreobjects from the population, e.g. V′=V_(μ)+pc, where c is set of weightsthat determine the differences from mean shape according to modes ofallowable variation.

While the theory of shape models is well established, the presentedmethod utilizes the theory to fit a mean surface into the sparse imageset acquired such that entire object can be represented using just a fewnumber of 2-D images. Generally, to perform segmentation directly on theimage will require a reasonably good resolution of image, which meansthat a large number of images will need to be acquired and 3-D imagespace will need to be filled before segmentation can be done. Inaddition, segmentation in 3-D is computationally demanding and may notbe able to be performed within a desirable time. As a result, sometechniques resort to segmentation in 2-D and then combining the 2-Dsegmentations into a surface in 3-D. Such techniques, while faster,obviously suffer from disadvantages of not using the availableinformation fully by neglecting the 3-D interconnectivity.

In presented method, a few numbers of images are sufficient to perform agood segmentation of the object. This is achieved by first acquiring afew images that captures the object in various orientations and thenplacing the mean shape 900 into the reconstructed 3-D image 904. SeeFIG. 3C. The reconstructed 3-D image will be very sparse with mostlyzero values. However, only available 2-D information in variousorientations can be used to deform the mean shape 900 into the shape ofthe scanned object by letting the mean surface deform while followingthe population deformation characteristics (modes of variations). Thedeformation optimizes the value of coefficients “c” such thatV′=V_(μ)+pc yields maximization of intensity gradient at the boundariesof the surface. For instance, the edges 910 of the object as representedby the acquired image frames may be utilized as constraints for definingthe mean shape. These edges may be identified using any appropriatesegmentation process. In any case, use of the edges 910 allows forclosely fitting the mean shape to the actual object boundaries. See FIG.3D.

Motion Correction in 3-D

In a number of procedures, an image of one modality is scanned in 3-Dbefore the procedure (pre-op) and then a 2-D live image is used tonavigate during the procedure. Examples include pre-op MR scan before asurgery (say, brain) and live 2-D ultrasound image while performing theactual surgery. Due to differences in time between the two images,difference in coordinate system, differences in image qualities anddeformation of tissue between the images or during the procedure, it isimportant to align the 2-D live ultrasound image with the previouslyacquired 3-D images, which may be stored by an imaging device. SeeFIG. 1. Any motion of patient, instrument or tissue can complicate theproblem. Most current techniques align 2-D live image with a stored 3-Dimage by extracting corresponding 2-D slice from the 3-D image, which isequivalent to a rigid registration between the images. Since theregistration is based on only a 2-D image slice, there can be many falseminima and robustness is an issue. Moreover, a non-rigid registrationthat may follow, assumes only in-plane deformation, which captures onlya part of actual deformation. In the presented invention, the robustnessis improved by using a series of 2-D slices for motion correction suchthat the registration is based on partial 3-D information rather thanpurely 2-D information.

The real-time interpolation from arbitrary image planes, as discussedabove, easily allows for such a system, where the 2-D live image beingacquired is kept in buffer for next few frames. For instance, at anytime the 2-D live image slice number is n, and there may always beprevious m images kept in a buffer. m may be a small number such as5-10. In this case, there are more than one slices to be placed in 3-D.Since the data input refresh rate is typically around 30 frames persecond, 5-10 slices only means that the data corresponding to a smallfraction (≅0.2 seconds) is being kept in buffer. This immediate previousdata, together with current frame provides 5-10 slices in 3-D, that canbe correlated with a previously acquired 3-D image. Since there are moreslices in different orientation, the robustness is much improved and thelive images can be placed in coordinate frame of reference of the 3-Dimage with more confidence. In addition, if non-rigid registration isneeded, the sequence of images provide a much better starting point thanjust one image and capture deformation in more directions than justalong the frame of image acquisition.

Acquisition of Different Modalities and Their Fusion

The image acquisition method discussed above can be used for diagnosticimaging or for biopsy, surgical or image based drug delivery system. Theinterpolation method may be used to acquire images from differentmodalities such as ultrasound and elastography. The 2-D image acquiredfrom these modalities can be combined together with the trackinginformation to place the results in 3-D such that the elastographyimages can be shown overlaid on the ultrasound data, thus providingadditional information to the user. In addition, if a pre-operative MRIscan is available in 3-D, the reconstructed elastographic data can beoverlaid onto the structural scan acquired earlier. Likewise, iffunctional information such as MR spectroscopy is available, the liveultrasound image can be placed in correspondence with the spectroscopydata based on the tracker information and the registration step asdiscussed above. The registration may be two-step process: rigid,followed by non-rigid. The non-rigid registration may be based on thesegmented surface, where a shape model can be used to segment the imagesin different modalities and the segmented surfaces registered together.

In addition to registering images from different modalities to perform afusion, the presented method may also be used to study local tissuedeformations over time. Many diseases manifest themselves in abnormaltissue deformation and the local deformation can be used forinterpreting tissue conditions. As an example, in a repeat prostatebiopsy case, a patient image obtained from previous visit may beregistered with the image obtained from the repeat visit and the localdeformation may be used to observe the abnormal local volume changes intissue. The local deformation may be a useful indicator of locations ofcancer growth. The registration may be mutual information based,intensity based, surface based, landmark based or a combination of anyof these. The registration provides the correspondence between theprevious image and the current image. The Jacobian value for thedeformation map may then be computed and overlaid on the 3-D prostatevolume to look at the abnormal localized deformations and any suchlocations found must be sampled.

The above-noted utilities provide a number of advantages. One primaryadvantage is that the utilities can construct 3-D images using 2-Dimages that are acquired in an unconstrained manner (e.g., freehand)where uniform angular or linear spacing between images is no longerrequired. Likewise, this may result in better workflow as a user nolonger needs to obtain images in a constrained environment.

Another advantage is that the utilities allow for flexibility ofresolution. In this regard, the user can scan different regions withdifferent resolution thereby providing more flexibility and usage. Thisalso permits fine tuning of an image in real-time. If the user desiresbetter resolution for an acquired image, they may scan more images ofthe same anatomy and have those images applied to the reconstructedimage.

The foregoing description of the present invention has been presentedfor purposes of illustration and description. Furthermore, thedescription is not intended to limit the invention to the form disclosedherein. Consequently, variations and modifications commensurate with theabove teachings, and skill and knowledge of the relevant art, are withinthe scope of the present invention. The embodiments describedhereinabove are further intended to explain best modes known ofpracticing the invention and to enable others skilled in the art toutilize the invention in such, or other embodiments and with variousmodifications required by the particular application(s) or use(s) of thepresent invention. It is intended that the appended claims be construedto include alternative embodiments to the extent permitted by the priorart.

1. A method for use in medical ultrasound imaging, comprising: obtaininga first two dimensional image of an internal object of interest, saidfirst two dimensional image having a first image plane and a first threedimensional point of reference; obtaining a second two dimensionalmedical image of the internal object of interest, said first twodimensional image having a second image plane and second threedimensional point of reference, wherein said first and second threedimensional points of reference are different and said image planes aretransverse; translating pixel information from said first and second twodimensional medical images into a common three dimensional volume; andgenerating a three dimensional image of the object of interest from saidpixel information as translated into said three-dimensional volume. 2.The method of claim 1, wherein said first and second two dimensionalimages are obtained arbitrarily using freehand scanning.
 3. The methodof claim 1, wherein translating pixel information further comprises:applying a coordinate transform to said two-dimensional images.
 4. Themethod of claim 3, wherein applying a coordinate transform furthercomprises: for each two-dimensional image determining a normal to saidimage plane; defining unit vectors in three directions for said twodimensional image; and applying a rotation matrix to said vectors. 5.The method of claim 1, wherein translating said pixel intensity furthercomprises: interpolating pixel intensities from said two-dimensionalimages to discrete locations in said three-dimensional volume.
 6. Themethod of claim 1, further comprising: after generating said threedimensional image, obtaining an additional i^(th) two dimensional imagehaving an i^(th) image plane and an i^(th) three dimensional point ofreference; and incorporating pixel information of said i^(th) image intosaid three-dimensional image.
 7. The method of claim 6, whereinobtaining an additional i^(th) two dimensional image further comprises:selectively obtaining a two-dimensional image for a location of interestof said internal object of interest.
 8. The method of claim 6, whereinsaid three dimensional image contains different resolution in at leastfirst and second regions of said three dimensional image.
 9. The methodof claim 1, wherein for each image obtaining comprises: receiving animage plane from an ultrasound imaging device; and receiving locationinformation from a tracker device attached to said ultrasound imagingdevice.
 10. The method of claim 1, wherein for each image obtainingcomprises: receiving an image plane from a hand-guided ultrasoundimaging device.
 11. The method of claim 1,wherein generating a threedimensional image of said object of interest comprises interpolating asurface of said object of interest using statistics associated with saidobject of interest.
 12. The method of claim 11, wherein interpolatingsaid surface comprises: fitting a predefined shape model to said pixelinformation translated into said common three dimensional volume.
 13. Amethod for use in medical ultrasound imaging, comprising: obtaining aplurality of arbitrarily aligned two dimensional ultrasound images,wherein said images are of an internal object of interest; translatingpixel intensity information from each of said two dimensional ultrasoundimages into a common three dimensional volume; fitting a predefinedshape model to said pixel intensity information of said common threedimensional volume, wherein said shape model defines a three dimensionalsurface for said object of interest.
 14. The method of claim 13, whereinsaid predefined shape model is based on an average of a correspondingobject of a training set population.
 15. The method of claim 14, whereinshape statistics of said training set population are utilized to fitsaid predefined shape model to said pixel intensity information.
 16. Themethod of claim 13, wherein boundaries identified within said twodimensional images form constraints for fitting said predefined shapemodel.
 17. The method of claim 13, further comprising: performing athree dimensional segmentation of said object of interest using saidthree dimensional surface as an initial surface boundary.
 18. A methodfor use in medical ultrasound imaging, comprising: obtaining a series oftwo dimensional images, wherein said images are of an internal object ofinterest; translating pixel information from each of said twodimensional ultrasound images into a common three dimensional volume;generating a current three dimensional image of the object of interestfrom said pixel information as translated into said three dimensionalvolume; and using said current three dimensional image of the object ofinterest to align the most current two dimensional image of said seriesof two dimensional images with a previously stored three dimensionalimage of said object of interest.
 19. The method of claim 18, whereinusing said current three dimensional image comprises registering saidcurrent tree dimensional image with said previous three dimensionalimage.
 20. The method of claim 18, wherein said series of twodimensional images are maintained in a buffer.
 21. The method of claim20, wherein said buffer maintains said most current two dimensionalimage and between 5 and 10 immediately previous two dimensional imagesin storage.